{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7RiatjU_ra0b"
      },
      "source": [
        "# Using vLLM for Inference with Yi-1.5-6B-Chat\n",
        "\n",
        "Welcome to this tutorial! Here, we will guide you through using vLLM for inference with the Yi-1.5-6B-Chat model. vLLM is a fast and easy-to-use library for large language model (LLM) inference and serving. Let's get started!"
      ],
      "id": "7RiatjU_ra0b"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EVlAACVkra0e"
      },
      "source": [
        "## 🚀 Running on Colab\n",
        "\n",
        "We also provide a one-click [Colab script](https://colab.research.google.com/drive/1KuydGHHbI31Q0WIpwg7UmH0rfNjii8Wl?usp=drive_link) to make development easier!"
      ],
      "id": "EVlAACVkra0e"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MgWrPYPVra0f"
      },
      "source": [
        "## Installation\n",
        "\n",
        "First, we need to install the required dependencies. According to the official documentation, installing vLLM with pip requires CUDA 12.1. You can refer to the official [documentation](https://docs.vllm.ai/en/stable/getting_started/installation.html) for more details.\n",
        "\n",
        "Let's install vLLM now:"
      ],
      "id": "MgWrPYPVra0f"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "installation",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16227258-d2bd-4cf9-fcf6-170ecc98866f"
      },
      "source": [
        "!pip install vllm"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hInstalling collected packages: pyairports, nvidia-ml-py, ninja, xxhash, websockets, uvloop, requests, python-multipart, python-dotenv, pycountry, pyarrow, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, lark, interegular, httptools, h11, dnspython, diskcache, dill, watchfiles, uvicorn, tiktoken, starlette, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, httpcore, email_validator, prometheus-fastapi-instrumentator, nvidia-cusolver-cu12, lm-format-enforcer, httpx, ray, openai, fastapi-cli, datasets, xformers, vllm-flash-attn, outlines, fastapi, vllm\n",
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            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.20.0 dill-0.3.8 diskcache-5.6.3 dnspython-2.6.1 email_validator-2.2.0 fastapi-0.111.1 fastapi-cli-0.0.4 h11-0.14.0 httpcore-1.0.5 httptools-0.6.1 httpx-0.27.0 interegular-0.3.3 lark-1.1.9 lm-format-enforcer-0.10.3 multiprocess-0.70.16 ninja-1.11.1.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-ml-py-12.555.43 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 openai-1.37.0 outlines-0.0.46 prometheus-fastapi-instrumentator-7.0.0 pyairports-2.1.1 pyarrow-17.0.0 pycountry-24.6.1 python-dotenv-1.0.1 python-multipart-0.0.9 ray-2.32.0 requests-2.32.3 starlette-0.37.2 tiktoken-0.7.0 uvicorn-0.30.3 uvloop-0.19.0 vllm-0.5.3.post1 vllm-flash-attn-2.5.9.post1 watchfiles-0.22.0 websockets-12.0 xformers-0.0.27 xxhash-3.4.1\n"
          ]
        }
      ],
      "id": "installation"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QL6vv0Vara0h"
      },
      "source": [
        "## Loading the Model\n",
        "\n",
        "Now, we'll load the Yi-1.5-6B-Chat model. Please be mindful of your computer's VRAM and disk space usage. If you encounter any errors, it might be due to insufficient resources.\n",
        "\n",
        "For this tutorial, we will use the Yi-1.5-6B-Chat model. Here's the VRAM and disk space usage for this model:\n",
        "\n",
        "| Model | VRAM Usage | Disk Space Usage |\n",
        "|-------|------------|------------------|\n",
        "| Yi-1.5-6B-Chat | 21G | 15G |"
      ],
      "id": "QL6vv0Vara0h"
    },
    {
      "cell_type": "code",
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        "id": "load-model",
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          ]
        },
        "outputId": "451e94be-0030-4a9e-8dc0-c8264acd8290"
      },
      "source": [
        "from transformers import AutoTokenizer\n",
        "from vllm import LLM, SamplingParams\n",
        "\n",
        "# Load the tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"01-ai/Yi-1.5-6B-Chat\")\n",
        "\n",
        "# Set the sampling parameters\n",
        "sampling_params = SamplingParams(\n",
        "    temperature=0.8,\n",
        "    top_p=0.8)\n",
        "\n",
        "# Load the model\n",
        "llm = LLM(model=\"01-ai/Yi-1.5-6B-Chat\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.67k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "25e5eec2a83044f6b40a2faa79aeb8e6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/1.03M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "81f23d4c79e24ee7acf70868d8de83b0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8fc000ca2046402a8e79aff404c126b3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/567 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "540ee9ec4a95472bbe76a9184c1bcfd2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/661 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3217fa33122b442994340a353d7896d6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO 07-25 02:22:07 llm_engine.py:176] Initializing an LLM engine (v0.5.3.post1) with config: model='01-ai/Yi-1.5-6B-Chat', speculative_config=None, tokenizer='01-ai/Yi-1.5-6B-Chat', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=4096, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None), seed=0, served_model_name=01-ai/Yi-1.5-6B-Chat, use_v2_block_manager=False, enable_prefix_caching=False)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/132 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "27fc772396df4317b5ab94083281d772"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO 07-25 02:22:08 model_runner.py:680] Starting to load model 01-ai/Yi-1.5-6B-Chat...\n",
            "INFO 07-25 02:22:09 weight_utils.py:223] Using model weights format ['*.safetensors']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00003.safetensors:   0%|          | 0.00/4.93G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3b2e3a1161244bb3b24b82dfe80ae662"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00003-of-00003.safetensors:   0%|          | 0.00/2.21G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c1f1fc0f332d4ffca74180489a04b761"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00003.safetensors:   0%|          | 0.00/4.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0538b713efe14e56b0a3a8ff3c7c678c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/23.9k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "52f266120eea492bb638eb01d17f7ecf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading safetensors checkpoint shards:   0% Completed | 0/3 [00:00<?, ?it/s]\n"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b7fd75e2f90e40f2865eb134d3a466d2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO 07-25 02:25:43 model_runner.py:692] Loading model weights took 11.2905 GB\n",
            "INFO 07-25 02:25:45 gpu_executor.py:102] # GPU blocks: 8089, # CPU blocks: 4096\n",
            "INFO 07-25 02:25:47 model_runner.py:980] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.\n",
            "INFO 07-25 02:25:47 model_runner.py:984] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.\n",
            "INFO 07-25 02:25:58 model_runner.py:1181] Graph capturing finished in 12 secs.\n"
          ]
        }
      ],
      "id": "load-model"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "smfEixsFra0i"
      },
      "source": [
        "## Model Inference\n",
        "\n",
        "Let's prepare a prompt template and perform inference using the model. We'll use a simple greeting prompt for this example."
      ],
      "id": "smfEixsFra0i"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inference",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "086ba952-9bd5-4f17-dce5-60968d3b8d64"
      },
      "source": [
        "# Prepare the prompt template\n",
        "prompt = \"Hi!\"  # Change the prompt as needed\n",
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": prompt}\n",
        "]\n",
        "text = tokenizer.apply_chat_template(\n",
        "    messages,\n",
        "    tokenize=False,\n",
        "    add_generation_prompt=True\n",
        ")\n",
        "print(text)\n",
        "\n",
        "# Generate the response\n",
        "outputs = llm.generate([text], sampling_params)\n",
        "\n",
        "# Print the output\n",
        "for output in outputs:\n",
        "    prompt = output.prompt\n",
        "    generated_text = output.outputs[0].text\n",
        "    print(f\"Prompt: {prompt!r}, Generated text: {generated_text!r}\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|im_start|>user\n",
            "Hi!<|im_end|>\n",
            "<|im_start|>assistant\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processed prompts: 100%|██████████| 1/1 [00:00<00:00,  1.52it/s, est. speed input: 15.19 toks/s, output: 19.75 toks/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt: '<|im_start|>user\\nHi!<|im_end|>\\n<|im_start|>assistant\\n', Generated text: \"你好！有什么我可以帮助你的吗？']\\n````\"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "id": "inference"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ZITsk_Fra0j"
      },
      "source": [
        "That's it! You've successfully performed inference using vLLM with the Yi-1.5-6B-Chat model. Feel free to experiment with different prompts and adjust the sampling parameters to see how the model responds. Happy experimenting!"
      ],
      "id": "4ZITsk_Fra0j"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
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